def calculate_DER(meeting):
meeting_base = get_meeting_base(meeting)
labels = pd.read_csv(os.path.join(labels_dir, meeting_base))
true_seq = labels['combined']
# load hmm states
hmm_states = None
with open(meeting, 'rb') as infile:
hmm_states = cPickle.load(infile)
stateseq = hmm_states.stateseq
diarization_error, best_seq, _ = hdphmm_utils.find_error_rate(stateseq, true_seq)
hdphmm_utils.plot_pred_labels(meeting_base, best_seq, labels_dir)
# print('DER: {}'.format('{0:.3f}'.format(diarization_error)))
# if diarization_error > 0.8:
# ipdb.set_trace()
return diarization_error
def find_states(features_file):
meeting_base = features_file.split('/')[-1]
print(meeting_base)
data = np.genfromtxt(features_file, delimiter=',')
# data = np.load(features_file)
labels = pd.read_csv(os.path.join(labels_dir, meeting_base))
true_seq = labels['combined']
# features_file = open(features_file, 'rb')
# data = pickle.load(features_file)
##########################
# Sticky-HDP-HMM #
##########################
# and some hyperparameters
obs_dim = data.shape[1]
obs_hypparams = {'mu_0':np.zeros(obs_dim),
'sigma_0':np.eye(obs_dim),
'kappa_0':0.25,
'nu_0':obs_dim+2}
# create a bunch of multivariate gaussians
obs_distns = [pyhsmm.distributions.Gaussian(**obs_hypparams) for state in xrange(Nmax)]
# parameters for priors taken from Fox 2012
gamma_draw = gamma(12,2)
alpha_plus_kappa_draw = gamma(6,1)
sigma_draw = gamma(1,0.5)
rho_draw = beta(500,5)
# can deterministically retrieve kappa and alpha from draws for alpha+kappa and rho
kappa = rho_draw * alpha_plus_kappa_draw
alpha = (1-rho_draw) * alpha_plus_kappa_draw
print('kappa: {}, alpha: {}, gamma: {}'.format(kappa, alpha, gamma_draw))
# ipdb.set_trace()
obs_hypparams = {'mu_0':np.zeros(obs_dim),
'sigma_0':np.eye(obs_dim),
'kappa_0':0.3,
'nu_0':obs_dim+5}
dur_hypparams = {'alpha_0':2*30,
'beta_0':2,
'lmbda':2.5
}
obs_distns = [pyhsmm.distributions.Gaussian(**obs_hypparams) for state in range(Nmax)]
dur_distns = [pyhsmm.distributions.PoissonDuration(**dur_hypparams) for state in range(Nmax)]
# ipdb.set_trace()
posteriormodel = pyhsmm.models.WeakLimitStickyHDPHMM(
# NOTE: instead of passing in alpha_0 and gamma_0, we pass in parameters
# for priors over those concentration parameters
kappa=kappa,alpha=alpha,gamma=gamma_draw,
init_state_concentration=6.,
obs_distns=obs_distns)
# ipdb.set_trace()
# posteriormodel = pyhsmm.models.WeakLimitHDPHSMM(
# alpha=alpha,gamma=gamma_draw,init_state_concentration=1.,
# obs_distns=obs_distns,
# dur_distns=dur_distns)
# data = np.zeros(data.shape)
# posteriormodel = pyhsmm.models.WeakLimitHDPHSMM(
# # NOTE: instead of passing in alpha_0 and gamma_0, we pass in parameters
# # for priors over those concentration parameters
# alpha_a_0=1.,alpha_b_0=1./4,
# gamma_a_0=1.,gamma_b_0=1./4,
# init_state_concentration=6.,
# obs_distns=obs_distns,
# dur_distns=dur_distns)
posteriormodel.add_data(data)
# for idx in progprint_xrange(100):
# posteriormodel.resample_model()
# plt.figure()
# posteriormodel.plot()
# plt.gcf().suptitle('Sampled after 100 iterations')
# plt.figure()
# t = np.linspace(0.01,30,1000)
# plt.plot(t,scipy.stats.gamma.pdf(t,1.,scale=4.)) # NOTE: numpy/scipy scale is inverted compared to my scale
# plt.title('Prior on concentration parameters')
# plt.show()
# posteriormodel.add_data(data)
# # ipdb.set_trace()
num_cpu = 0
# # num_iterations = 1
all_trans_matrices = []
for idx in progprint_xrange(num_iterations):
posteriormodel.resample_model(num_procs=num_cpu)
trans_matrix = np.array([row.weights for row in posteriormodel.trans_distn._row_distns])
all_trans_matrices.append(trans_matrix)
# final_trans_matrix = np.mean(np.array(all_trans_matrices), axis=0) # average transition probs
# # ipdb.set_trace()
# for i in range(len(final_trans_matrix)):
# posteriormodel.trans_distn._row_distns[i].weights = final_trans_matrix[i]
# trans_matrix = np.array([row.weights for row in posteriormodel.trans_distn._row_distns])
# print trans_matrix
# posteriormodel.resample_model(num_procs=num_cpu)
# dump state sequence information
hmm_states = posteriormodel.states_list[0]
# ipdb.set_trace()
if not os.path.exists(output_dir):
os.makedirs(output_dir)
with open(os.path.join(output_dir, meeting_base + '.pickle'), 'wb') as outf:
cPickle.dump(hmm_states, outf)
num_states = len(set(hmm_states.stateseq_norep))
average_duration = np.average(hmm_states.durations) / 10.0
print('Num States: {}'.format(num_states))
print('Average Duration: {}'.format('{0:.2f}'.format(average_duration)))
diarization_error, best_seq, _ = hdphmm_utils.find_error_rate(hmm_states.stateseq, true_seq)
diarization_error = '{0:.3f}'.format(diarization_error)
print('DER: {}'.format(diarization_error))
hdphmm_utils.plot_pred_labels(meeting_base, best_seq, labels_dir, plot_dir, diarization_error)
# EM PLOTTING
# plt.figure()
# posteriormodel.plots()
# plt.gcf().suptitle('Gibbs-sampled initialization')
# print 'EM'
# likes = posteriormodel.EM_fit()
# plt.figure()
# posteriormodel.plot()
# plt.gcf().suptitle('EM fit')
# plt.figure()
# plt.plot(likes)
# plt.gcf().suptitle('log likelihoods during EM')
# plt.show()
# DONE
# posteriormodel.plot()
# plt.gcf().suptitle('Sticky HDP-HMM sampled model: {}\n \
# Num States: {}, Avg Duration: {}s, Num Iterations: {}'\
# .format(filebase, num_states,
# '{0:.2f}'.format(average_duration), num_iterations))
# plt.savefig(os.path.join(output_dir, 'plots', filebase + '_' + str(Nmax) + '.png'))
# plt.show()
# plt.cla()
# plt.clf()
print('')
return float(diarization_error)
